Fuzzy C-Means

List of Articles Fuzzy C-Means

Open Access Article
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1 - Efficient Land-cover Segmentation Using Meta Fusion
Morteza Khademi Hadi Sadoghi Yazdi
10.7508/jist.2016.03.003

Most popular fusion methods have their own limitations; e.g. OWA (order weighted averaging) has “linear model” and “summation of inputs proportions in fusion equal to 1” limitations. Considering all possible models for fusion, proposed fusion method involve input data c More

Most popular fusion methods have their own limitations; e.g. OWA (order weighted averaging) has “linear model” and “summation of inputs proportions in fusion equal to 1” limitations. Considering all possible models for fusion, proposed fusion method involve input data confusion in fusion process to segmentation. Indeed, limitations in proposed method are determined adaptively for each input data, separately. On the other hand, land-cover segmentation using remotely sensed (RS) images is a challenging research subject; due to the fact that objects in unique land-cover often appear dissimilar in different RS images. In this paper multiple co-registered RS images are utilized to segment land-cover using FCM (fuzzy c-means). As an appropriate tool to model changes, fuzzy concept is utilized to fuse and integrate information of input images. By categorizing the ground points, it is shown in this paper for the first time, fuzzy numbers are need and more suitable than crisp ones to merge multi-images information and segmentation. Finally, FCM is applied on the fused image pixels (with fuzzy values) to obtain a single segmented image. Furthermore mathematical analysis and used proposed cost function, simulation results also show significant performance of the proposed method in terms of noise-free and fast segmentation. Manuscript profile
Open Access Article
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2 - Proposing an FCM-MCOA Clustering Approach Stacked with Convolutional Neural Networks for Analysis of Customers in Insurance Company
Motahareh Ghavidel meisam Yadollahzadeh tabari Mehdi Golsorkhtabaramiri
10.61186/jist.41465.12.45.62

To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimension More

To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimensional with too many instances and include both supervised and unsupervised ones. For this reason, companies today are trying to satisfy their customers as much as possible. This issue requires careful consideration of customers from several aspects. Data mining algorithms are one of the practical methods in businesses to find the required knowledge from customer’s both demographic and behavioral. This paper presents a hybrid clustering algorithm using the Fuzzy C-Means (FCM) method and the Modified Cuckoo Optimization Algorithm (MCOA). Since customer data analysis has a key role in ensuring a company's profitability, The Insurance Company (TIC) dataset is utilized for the experiments and performance evaluation. We compare the convergence of the proposed FCM-MCOA approach with some conventional optimization methods, such as Genetic Algorithm (GA) and Invasive Weed Optimization (IWO). Moreover, we suggest a customer classifier using the Convolutional Neural Networks (CNNs). Simulation results reveal that the FCM-MCOA converges faster than conventional clustering methods. In addition, the results indicate that the accuracy of the CNN-based classifier is more than 98%. CNN-based classifier converges after some couples of iterations, which shows a fast convergence in comparison with the conventional classifiers, such as Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Naive Bayes (NB) classifiers. Manuscript profile

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List of Articles Fuzzy C-Means